Big drilling data analytics engine

ABSTRACT

The invention relates to systems, processes and apparatuses for determining a rig-state of a drilling rig during a wellbore drilling operation and detecting and mitigating drilling dysfunctions. These systems, processes and apparatuses provide a computer with a memory and a processor, a plurality of sensors associated with a wellbore drilling operation for acquiring time series data wherein the data are formatted for sample and bandwidth regularization and time-corrected to provide substantially time-synchronized data, a processing graph of data-stream networked mathematical operators that applies continuous analytics to the data at least as rapidly as the data are acquired to determine dynamic conditions of a plurality of rig conditions associated with the wellbore drilling operation and determining a rig-state from the plurality of rig conditions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional application which claims benefitunder 35 USC § 119(e) to U.S. Provisional Application Ser. No.62/160,998 filed May 13, 2015, entitled “BIG DRILLING DATA ANALYTICSENGINE,” which is incorporated herein in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

None.

FIELD OF THE INVENTION

The present invention relates generally to detection, quantification andmitigation of dysfunctions in drilling for hydrocarbons. Moreparticularly, but not by way of limitation, embodiments of the presentinvention include applying analytics to real-time data acquired fromwellbore drilling operations to mitigate drilling dysfunctions.

BACKGROUND OF THE INVENTION

Hydrocarbon reservoirs are developed with drilling operations using adrill bit associated with a drill string rotated from the surface orusing a downhole motor, or both using a downhole motor and also rotatingthe string from the surface. A bottom hole assembly (BHA) at the end ofthe drill string may include components such as drill collars,stabilizers, drilling motors and logging tools, and measuring tools. ABHA is also capable of telemetering various drilling and geologicalparameters to the surface facilities.

Resistance encountered by the drill string in a wellbore during drillingcauses significant wear on drill string, especially often the drill bitand the BHA. Understanding how the geometry of the wellbore affectsresistance on the drill string and the BHA and managing the dynamicconditions that lead potentially to failure of downhole equipment isimportant for enhancing efficiency and minimizing costs for drillingwells. Various conditions referred to as drilling dysfunctions that maylead to component failure include excessive torque, shocks, bit bounce,induced vibrations, bit whirl, stick-slip, bit-bounce among others.These conditions must be rapidly detected so that mitigation efforts areundertaken as quickly as possible, since some dysfunctions can quicklylead to tool failures.

Rapid aggregation and analysis of data from multiple sources associatedwith well bore drilling operations facilitates efficient drillingoperations by timely responses to drilling dysfunctions. Accurate timinginformation for borehole or drill string time-series data acquired withdown hole sensors are important for aggregating information from surfaceand down hole sensors. However, each sensor may have its own internalclock or data from many sensors may be acquired and recorded relative tomultiple clocks that are not synchronized. This non-synchronization ofthe timing information creates problems when combining and processingdata from various sensors. Additionally, sensor timing is knownsometimes to be affected by various environmental factors that causevariable timing drift that may differentially impact various sensors.Many factors may render inaccurate the timing of individual sensors thatthen needs to be corrected or adjusted so the data may be assimilatedcorrectly with all sensor information temporally consistent in order toaccurately inform a drilling operations center about the dynamic stateof the well being drilled.

Downhole drilling dysfunctions can cause serious operational problemsthat are difficult to detect or predict. The more rapidly andefficiently drilling dysfunctions are identified the more quickly theymay be mitigated. Thus a need exists for efficient methods, systems andapparatuses to quickly identify and to mitigate dysfunctions duringdrilling operations.

BRIEF SUMMARY OF THE DISCLOSURE

It should be understood that, although an illustrative implementation ofone or more embodiments are provided below, the various specificembodiments may be implemented using any number of techniques known bypersons of ordinary skill in the art. The disclosure should in no way belimited to the illustrative embodiments, drawings, and/or techniquesillustrated below, including the exemplary designs and implementationsillustrated and described herein. Furthermore, the disclosure may bemodified within the scope of the appended claims along with their fullscope of equivalents.

The invention more particularly includes in nonlimiting embodiments asystem for determining a rig-state of a drilling rig during a wellboredrilling operation comprises a computer comprising a memory and aprocessor, a plurality of sensors associated with a wellbore drillingoperation for acquiring time series data wherein the data are formattedfor sample and bandwidth regularization and time-corrected to providesubstantially time-synchronized data, a processing graph of data-streamnetworked mathematical operators that applies continuous analytics tothe data at least as rapidly as the data are acquired to determinedynamic conditions of a plurality of rig conditions associated with thewellbore drilling operation and determining a rig-state from theplurality of rig conditions.

In another nonlimiting embodiment, a process for determining a rig-stateof a drill rig comprises acquiring data from a plurality of sensorsassociated with a wellbore, formatting the acquired data for sample andbandwidth regularization, time-correcting the data to providesubstantially isochronously sampled data from the plurality of sensors,processing the acquired data through a processing graph of networkedmathematical operators that apply continuous analytics to the data atleast as rapidly as the data are acquired to determine dynamicconditions of a plurality of rig operations associated with the wellboreand determining a rig-state from the plurality of rig operationsconditions.

In still further nonlimiting embodiments a drilling rig apparatus formitigating drilling dysfunctions comprises a drill rig associated with aplurality of sensors providing time series data to a surface-basedaggregator wherein the data are formatted for sample and bandwidthregularization and time-corrected to provide substantiallytime-synchronized data, a computer comprising a memory and a processor,a processing graph of data-stream networked mathematical operators thatapplies continuous analytics at least as rapidly as the time-series areacquired to determine dynamic conditions of a plurality of rigconditions associated with wellbore drilling operation and detecting adrilling dysfunction from the plurality of rig conditions.

In yet more nonlimiting embodiments a computer program product isembodied in non-transitory computer readable media, the computer programproduct adapted to execute a process to mitigate a drilling dysfunctionduring a wellbore drilling operation, which comprises acquiring datafrom a plurality of sensors associated with a wellbore drillingoperation, formatting the acquired data for sample and bandwidthregularization, time-correcting the data to provide substantiallysynchronously sampled data from the plurality of sensors, processing theacquired data through a processing graph of networked mathematicaloperators that apply continuous analytics to the data at least asrapidly as the data are acquired to determine dynamic conditions of aplurality of rig operations associated with the wellbore, detecting adrilling dysfunction from the plurality of rig operations conditions,and outputting drill rig control instructions to mitigate the detecteddrilling dysfunction.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and benefitsthereof may be acquired by referring to the follow description taken inconjunction with the accompanying drawings in which:

FIG. 1 illustrates an example of drilling operations in according tovarious embodiments of the present disclosure;

FIG. 2 schematically illustrates a processing graph according to variousembodiments of the present disclosure;

FIG. 3 illustrates parameters related to a geometrical tortuositybending function;

FIG. 4 illustrates forces relative to a bending drill pipe;

FIG. 5 illustrates a process for determining real-time drillingdysfunctions by measuring power-loss of signal propagation associatedwith a drill string according to various embodiments of the presentdisclosure;

FIG. 6 illustrates a system associated with a drill string in a wellborefor acquiring a time series according to various embodiments of thepresent disclosure;

FIG. 7 illustrates the use of a drilling apparatus for drilling multiplewells according to various embodiments of the present disclosure;

FIG. 8 illustrates an example of time-series data before and after timecorrection of the data according to various embodiments of the presentdisclosure;

FIG. 9 illustrates an example before and after clock-drift correction ofdownhole data according to various embodiments of the presentdisclosure;

FIG. 10 illustrates an example before and after linear moveoutcorrection of data acquired from downhole transducers according tovarious embodiments of the present disclosure;

FIG. 11 illustrates a method according to embodiments of the presentdisclosure for adjusting time series data relative to a reference timeaccording to various embodiments of the present disclosure;

FIG. 12 illustrates a method according to alternative embodiments of thepresent disclosure for adjusting time series data relative to areference time according to various embodiments of the presentdisclosure;

FIG. 13 illustrates a method according to further embodiments of thepresent disclosure for automatically adjusting time series data relativeto a reference time according to various embodiments of the presentdisclosure;

FIG. 14 illustrates a schematic diagram of an embodiment of a systemthat may correspond to or may be part of a computer according to variousembodiments of the present disclosure;

FIG. 15 illustrates a system for determining a rig-state of a drillingrig during a wellbore drilling operation according to variousembodiments of the present disclosure; and

FIG. 16 illustrates a process for determining a rig-state of a drill rigduring a wellbore drilling operation according to various embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Turning now to the detailed description of the preferred arrangement orarrangements of the present invention, it should be understood that theinventive features and concepts may be manifested in other arrangementsand that the scope of the invention is not limited to the embodimentsdescribed or illustrated. The scope of the invention is intended only tobe limited by the scope of the claims that follow.

The following examples of certain embodiments of the invention aregiven. Each example is provided by way of explanation of the invention,one of many embodiments of the invention, and the following examplesshould not be read to limit, or define, the scope of the invention.

Mitigating drilling dysfunctions in oil-field drilling operations is apriority in the industry. Low-frequency surface data, such as RPM,torque, and acceleration data, are routinely used to mitigate drillingdysfunctions. Recent developments in recording high-frequency surface aswell as downhole data provides for better detection, analysis and leadto more rapid mitigation of drilling dysfunctions. Complex EventProcessing (CEP) is provided through data acquisition and processingcapabilities that are encompassed within embodiments disclosed herein.Real time analytics are possible when tool motion and dysfunctionindices are analyzed during drilling operations using signal processing,vibration analysis, CEP and feedback loops, including instructions tomitigate dysfunctions, to rig controls. This leads to efficientacquisition of downhole tool wear indices through monitoring andprediction, which allows for optimized preventive maintenance on allparts of the string. This also allows for an effectively continuousunderstanding of downhole conditions, resulting in a wellbore that isoptimized for completions. Useful indices for the analytics engineinclude dysfunction indices such as Stick Slip Index (SSI), Bit BounceIndex (BBI), Bit Whirl Index (BWI) and Mechanical Specific Energy (MSE).

The continuous real time acquisition of multiple data streams ofconventional and new data types allows for analytic measures for eachdirectly acquired time series as well as the combinations of these datatogether as rapidly and efficiently as possible. How these measurementschange over time and how each measurement data stream changes relativeto other data streams provides new analytic tools to understand thedrill string dynamics and wellbore conditions as the data are acquired.

Proper merging and analysis of data is integral to understanding CEP andcreating rules or learnings applicable to current drilling operations.This merging and comparison of data is impaired when different datatypes are not synchronized to the same reference time.

In drilling operations, sensors are placed at different wellborelocations, drill string locations and time intervals to providereal-time measurements such as revolutions per minute (RPM), torques,weight-on-bit (WOB) and accelerations. The data acquired with thesesensors may be irregularly distributed and subject to transmissionlosses due to absorption, scattering, and leakage induced by bendingeffects of the well trajectory. The nonlinear combination of theseeffects causes an important attenuation or power-loss of signalamplitudes that may compromise the integrity and prediction ofdysfunctions taking place at multiple sections of the drill string.

FIG. 1 illustrates an example of drilling a subterranean formation witha first wellbore and a second wellbore according to various embodimentsof the present disclosure. The various embodiments disclosed herein areused in the well drilling environment as illustrated in FIG. 1 wherein awell bore 102 is drilled from surface drilling rig facilities 101comprising a drilling rig, drill string associated surface-based sensors103 to obtain data from within the wellbore, for example an electronicacoustic receiver attached on the Kelly or BOP, as well as associatedcontrol and supporting facilities, 105, which may include dataaggregation, data processing infrastructure including computer systemsas well as drilling control systems. During drilling operations the wellbore 102 includes a drill string comprising an associated bottom holeassembly (BHA) that may include a mud motor 112, an adjustable benthousing or ‘BHA Dynamic Sub’ 114 containing various sensors, transducersand electronic components and a drill bit 116. The BHA Dynamic Subacquire time series data such as RPM, torque, bending moment, tension,pressure (ECS) and vibration data. Additionally, the BHA acquiresmeasurement-while-drilling and logging-while-drilling (MWD/LWD) data inhigh fidelity or standard modes, such as inclination, azimuth, gammaray, resistivity and other advanced LWD data. Any data acquired with theBHA may be transmitted to the drilling rig 101 through drill stringtelemetry or through mud-pulse telemetry as time series data.

The drill string may also contain associated sensors, for examplemid-string dynamic subs 110 that acquire high fidelity time series datasuch as RPM, torque, bending moment, tension and vibration data, andthese instrumented subs can send signals representing these measurementsby telemetry up the drill string where they are also recorded on or nearthe drilling rig 101.

In various embodiments, it is possible to increase the efficiency fordrilling a subsequent well by providing the results acquired drillingthe first wellbore 102 to be used in drilling of a second wellbore, suchas wellbore 104 of FIG. 1. Model parameters determined from drilling afirst wellbore 102, combined with the geometry information and othertime series data received by telemetry from the BHA associated with thedrill string for the second wellbore 104, may be used to determine thedownhole dynamics associated with the drilling operations, so thatdysfunctions may be quickly detected and mitigated effectively.

Embodiments disclosed herein provide for data-driven drillingperformance optimization. Performance optimization of a drillingoperation is a Big Data problem, requiring rapid (real time) integrationand analysis of wide varieties and large volumes of data streams.Relevant data are analyzed as rapidly as the data are acquired.Performance optimization translates into safer, more efficient andlower-cost drilling.

Embodiments of a Big Drilling Data Analytics Engine according to thepresent disclosure provide for a stream computing paradigm to flow datasamples through a processing graph of networked mathematical operators,interconnected by data streams. An example processing graph 200 isschematically illustrated in FIG. 2. Data ingestion is continuous withmany input data streams, flowing time-ordered and time-registered datasamples through the graph to apply continuous analytics. The AnalyticsEngine (processing graph) is provided access to data from all drillingoperation sensors, communication systems and hardware needed to streamdata in and out of it. Each mathematical operator is designed,positioned and connected within the flow of data such that the aggregateaction of the processing graph yields the desired analyses and outputstreams at each exit point of the processing graph. FIG. 2 schematicallydepicts the structure of input data streams to the processing graph, andoutput analytics and drill rig data control streams for exit 250, thoughit will be appreciated that a processing graph is not limited to oneexit. Output data streams include dysfunction indices, mechanicalspecific energy, rig state, borehole conditions, toolwear and rigcontrol feedback.

This Big Drilling Data Analytics Engine coordinates in “real time” theflow and analysis of all data streams from wellbore drilling operations.For historical analysis of recorded data, “real time” connotesoperations that are executed at least as rapidly as the sample rate ofdata.

As nonlimiting examples of inputs to the processing graph example ofFIG. 2, input 210 may comprise conventional measures for monitoring welldrilling operations such as hook load and torque. Input 220 may be highfidelity surface measurements such as RPM, torque, bending, tension,pressure and acceleration. Input 230 may comprise downhole measurementstelemetered to the surface such as MWD/LWD, temperature, directionalinformation, data stored in memory, and other ‘wired pipe’ informationboth stored and telemetered. Input 240, as a further example, maycomprise other data associated with drilling operations such as QualityAssurance/Quality Control data, WellView data and other commerciallyprovided information or data acquired from third party vendors onsiteassociated with the well operations.

Nonlimiting embodiments comprise data flow coordination and analysisthrough a processing graph that include real-time ingestion ofdiagnostic drilling data, feedback and drilling control parameters intoa processing graph of networked mathematical operators. Ingestionpreserves the time order of the input data streams and registers eachsample in the stream with the appropriate clock time. Data flowcoordination and analysis includes data formatting for flow through theprocessing graph, as well as for visualization at strategic positionswithin the graph and upon exiting the graph 250.

Sampling regularization accounts for variable sample rates from sensors.This regularization accommodates signal processing algorithms andvisualization software. For data streams that are downsampled, ananti-aliasing filter may be applied. Bandwidth regularization accountsfor variable resolution. Resolution often varies between sensor types,and can distort the results of data mining and analysis if not accountedfor.

Data flow coordination and analysis also includes “static” and “dynamic”time corrections to account for differences in time stamps betweendifferent sensors. “Static” errors are independent of the time stampcoordinate, “dynamic” errors are time variant. Static errors are mostoften introduced by human error, when the initial time stamp is setinaccurately for the data stream coming off a sensor. Dynamic timingerrors are often associated with clock drift, when the clocks on allsensors associated with rig time series measurements do not run at thesame rate.

Dynamic “moveout” corrections may be used to account for a travel timeof signal between sensors located at different physical locations. Areference time associated with a particular sensor or sensor group maybe arbitrarily selected, such as one at the surface, and all traveltimes corrected to the reference time, which may involve one or morecorrections, including moveout, static or dynamic corrections. Moreinformation about synchronization of times across sensor data isprovided below.

Data flow coordination and analysis through a processing graph areapplied to signal preprocessing of measured data streams to removeuninformative signal components. For example, acceleration sensorsattached to rotating equipment may contain uninformative signalcomponents as a consequence of rotation. These contributions are removedin real time to lay bare drilling dysfunction. Vibrationallyuninformative components may be targeted for mitigation algorithms.Real-time signal processing also maps the data from local, rotatingcoordinate systems to global, stationary coordinates.

Embodiments described herein provide for computing an output data streamof “rig state”. Rig state is a sample-by-sample automated categorizationof ongoing drill rig operations, computed from diagnostic input datastreams. Important categories include rotary drilling, sliding, reaming,back-reaming, tripping, etc. Data mining and analysis are supported byincorporating knowledge of rig state for each data sample.

Data flow coordination and analysis through a processing graph alsocomprises computing an output data stream that characterizes tortuosityof the wellbore, from wellbore position data measured as the well isdrilled. Points of high tortuosity in the wellbore generate largecontact forces on drill string components, producing undesirablevibration.

Data flow coordination and analysis through a processing graph alsoincludes an energy loss correction of surface-derived data streams toaccount for attenuation of diagnostic signals travelling to the surfacefrom downhole points of origin or, visa versa, travelling from thesurface to a downhole sensor. A weight on bit correction accounts forincomplete transfer of weight applied at the surface to weight on thedrill bit. The detection and quantification of drilling dysfunction(typically undesirable vibration and/or fluctuation in weight on bit)may be measured from diagnostic data streams from sensors deployedanywhere from surface equipment, through the drill string, to the bottomhole assembly and drill bit. Torque, acceleration, and tensionmeasurements constitute typical diagnostic data streams for detectingand quantifying undesirable modes of vibration during drillingoperations. Dysfunction mitigation algorithms and rig feedback controlmay focus on minimization of a dysfunction metric computed from inputdata streams, rather than directly on the input streams.

Data flow coordination and analysis through a processing graph furtherincludes output data streams of rig control instructions for alteringrpm, weight on bit applied at the surface, pump pressure, and othercontrollable drilling parameters, for the purpose of mitigating drillingdysfunction.

Embodiments disclosed herein provide for a Big Drilling Data AnalyticsEngine that coordinates the flow and conducts the analysis of measureddata streams from wellbore drilling operations. Embodiments of theAnalytics Engine comprise data-driven drilling performance optimization.Performance optimization in the drilling context includes reduction inundesirable mechanical vibration produced by the drilling operation,with a consequent reduction in trouble time. Drilling performancemetrics also include an optimal rate of penetration.

Optimization is achieved through the Analytics Engine by real-timeingestion and analysis of incoming diagnostic data streams. Driven byonline data analysis, commands can be issued in real-time to drillingrig controls to alter the RPM of the rotating drill string or, forexample, to alter weight applied to the bit. Real-time automated controlof RPM or weigh-on-bit parameters requires high-density (for example,100 samples/second) diagnostic data streams. This Analytics Engine iscapable of ingesting and analyzing several hundred high density datastreams simultaneously, consistent with what is known as a Big Dataproblem. These diagnostic data streams may be generated simultaneouslyfrom any part of the drilling operation, including sensors deployeddownhole or from equipment and operations at the surface.

Drilling optimization may also be achieved through historical analysisof recorded data to improve wellbore design, qualify new drillingtechnology, and establish data-driven best practices for futuredrilling. That is to say, the Analytics Engine is agnostic with respectto its source of data. It may be operated onsite in real time fromdirect sensor input at the drilling location, or, after-the-fact fromdata recorded in memory during the drilling operation and transmitted toan offsite operations center, or, some combination of the two. Whendeployed in an operations center receiving information from severalconcurrent wellbore drilling operation locations, the Big Drilling DataAnalytics Engine is capable of simultaneously analyzing high-densitydata from entire fleets of rigs.

The Big Drilling Data Analytics Engine provides an integrated platformfor high-speed data analysis and drilling operations performanceoptimization. It performs a wide range of interrelated analyses (e.g.,signal processing, dysfunction detection/characterization/mitigation,and data mining) in real time. It simultaneously analyzes streaming datafrom all sensors within the drilling system. The Analytics Engine may bedeployed within an offsite operations center or directly on a drillingrig.

Embodiments disclosed herein further provide for predicting real-timedrilling dysfunctions at any location of a drill string. The variousembodiments disclosed herein provide advantages that include: (a)simplicity to detect and model a wide range of possible power lossesthrough only three parameters; (b) determinations of down holeconditions that are well posed and amenable to stable estimation ofparameters at different scales; (c) flexibility for use with differentbending functions and signal representations (e.g., mean, envelopevalues); (d) efficiency for predicting dysfunctions by way of power-lossdeterminations at any point in time/depth, and therefore useful formeasuring and understanding dynamic downhole conditions throughmeasurements acquired at the surface drilling facilities associated withthe drill string, so that similarly situated wells may drilled withoutusing mid-string dynamic subs and only using surface acquired data tocharacterize the dynamic downhole environment during drillingoperations.

In drilling operations, sensors are placed at different wellborelocations, drill string locations and time/depth intervals to providereal-time measurements such as revolutions per minute (RPM), torques,weight-on-bit (WOB) and accelerations, etc. The data acquired with thesesensors may be irregularly distributed and subject to transmissionlosses due to absorption, scattering, and leakage induced by bendingeffects of the well trajectory. The nonlinear combination of thesegeometrical-related effects causes an important attenuation orpower-loss of signal amplitudes that may compromise the integrity andprediction of dysfunctions taking place at multiple sections of thedrill string along a wellbore.

An understanding of the laws governing the power-loss along the wellboreenables detection and enables drill rig control mechanisms that maymitigate undesirable vibrations or other conditions to prevent or delayeventual drill bit or BHA failures. The disclosed embodiments providesimple but powerful power-loss models that predict the decay of signalenergy under arbitrary bending effects due to the geometries of the wellbore. An understanding of the power-loss due to the wellbore geometryprovided by this power-loss model facilitates an understanding of thedynamic downhole conditions, including dysfunctions, as the well isbeing drilled.

The power-loss model depends on a set of three parameters: oneparameter, alpha (α), for controlling losses along the vertical section(i.e., regardless of bending effects) and two parameters, beta (β) andoptionally gamma (γ), that controls the trade-off between exponentialand hyperbolic signal decays for a given bending function or wellboregeometry.

The power-loss model combines analogs of slab (rigid) and fiber (soft)model losses that are similar to models proposed in Optics [Hunsperger,2009] and Photonics [Pollock, 2003]. The presently disclosed embodimentscomprise, but are not limited to, three different bending functionsrelative to wellbore geometries that may be described by mathematicalrelationships using α, β and γ: 1) a geometrical tortuosity, 2)cumulative dog-leg and 3) clamping efficiency.

Borehole tortuosity is inherent to drilling and is the undulation orvariance from the planned well bore trajectory, such as spiraling invertical sections or a slide-rotary behavior in horizontal sections. Adog-leg is a crooked place in a wellbore where the trajectory of thewellbore deviates from a straight path. A dog-leg may be createdintentionally in directional drilling to turn a wellbore to a horizontalpath, for example with nonconventional shale wells. The standardcalculation of dogleg severity is expressed in two-dimensional degreesper 100 feet, or degrees per 30 meters, of wellbore length.

The increasing use of sensors in real-time downhole operations is usefulto investigate the wellbore environment during the drilling process andto measure the actual geometry of the wellbore. The possibilities formodeling power-loss of signals travelling up the drill string as aresult of wellbore geometry may now be addressed in instrumenteddrilling practices. The models are generally governed by exponentialdecay functions. These functions may adopt different forms toaccommodate different types of materials, to capture other loss sourceson bending geometries such as those produced by micro-bending and suddenor relatively rapid changes in curvature.

Advantages of the bending function models disclosed herein include: (a)simplicity to accommodate a wide range of possible losses throughvarious mathematical descriptions using combinations of three modelparameters, herein designated as α, β and γ; (b) a well posed model ormodel group that is amenable to stable estimation of its parameters atdifferent scales; (c) flexibility to be used with different bendingfunctions and signal representations (e.g., mean, envelope values); and(d) efficiency for predicting dysfunction using the power-loss at anypoint in time/depth along the drill string leading to efficient andtimely dysfunction mitigation.

Low-frequency surface data, such as RPM, weight-on-bit (WOB), torque onbit (TOB) and acceleration data are routinely used to discover andmitigate drilling dysfunctions. However, recent developments inrecording high-frequency surface and downhole data adds a new dimensionto better understand drilling dysfunctions. Wave optics and photonicsliterature provide analogs useful for understanding transmission lossessuch as absorption, scattering and leakage through different materialsthat are subject to bending effects, such as are imposed by thegeometries within a wellbore.

In general, a loss that is due to curvature and other geometricalconsiderations in the well bore may be described by: P(z)=P(0)·e^(−αz),where P is power loss, z is depth and α is propagation of signalstrength in the drill string, so that

$a = {{- \frac{1}{P(z)}}{\frac{{dP}(z)}{dz}.}}$

Assuming that all propagation constants can be combined together andphase effects omitted, the signal propagation, a, may be expressed asα=α·e^(−β·R) (for the slab model case, useful for modeling overrelatively short distances) and as α=α·R^(−1/2)e−^(−β·R) (for the fibermodel case, useful for modeling over larger distances) where R is theradius of curvature, α is a situationally dependent magnitude constant,β and γ are parameters related to bending or radius in an exponential orhyperbolic sense.

Various embodiments of the present disclosure provide a HybridSlab/Fiber Model for Power-Loss. The disclosed model includes anexponential coefficient that decays as a mix of exponential andhyperbolic trends from a bending model whereinP(z=0)=P(z)·e^(−α(τ)z)=P(z)·e^(−αe) ^(−βτ) ^(τ) ^(−γ) ^(z) whereτ≡clamping efficiency. Note that for τ≅0=>P(z=0)=P(z)·e^(−α·z), which isthe attenuation model on a straight domain, such as the initial verticalsection of the well bore construction.

The two-step parameter estimation: (1) ln(P_(0,j)/P_(i,j))+α_(i)z_(i)=0i=1, 2, . . . , N_(z); j=1, 2, . . . , N_(s) and (2) α_(i)=αe^(−βτ) ^(i)τ_(i) ^(−γ), being the three-parameter problem to account for combinedslab/fiber effects where i is the index over depth and j indexes oversurvey stations.

The implementation of various preferred embodiments for characterizingor modeling the power-loss dysfunction includes an option to select ormodel a selected bending function (i.e., geometrical tortuosity, dog-legand clamping efficiency). Also, options to experiment with differentfitting options may be derived using these model parameters. Inaddition, it is possible to define fitting geometries from any givenstarting depth. There are also definitions provided by applications ofthe model parameters for different smoothing and filtering options. Slaband fiber models are available to estimate power-loss by inversion usinga combination of surface sensor time series data compared to equivalentdownhole sensor time series data. Regressions can be performed on datafor any sensor or aggregated data from some or all sensors.

The geometrical tortuosity bending function, ϑ, may be given by

${{\vartheta_{k} \equiv {1 - \frac{l_{k}}{z_{k}}}} = {1 - \frac{{{{TVD}_{k},{NS}_{k},{EW}_{k}}}_{2}}{{MD}_{k}}}},$where l_(k) is an idealized length from one subsurface survey stationposition to the next subsurface survey station position and z_(z) is theactual distance along the actual geometry length of the drilledwellbore. The numerator and denominator of the last term of thisequation are illustrated in FIG. 3. The cumulative dogleg bendingfunction, δ, is given by:

$\delta_{k} = {{arc}\;{{\cos\left( {{{\cos\left( i_{1,k} \right)} \cdot {\cos\left( i_{2,k} \right)}} + {{\sin\left( i_{1,k} \right)} \cdot {\sin\left( i_{2,k} \right)} \cdot {\cos\left( {{Az}_{2,k} - {Az}_{1,k}} \right)}}} \right)} \cdot {\frac{100}{{MD}_{k}}.}}}$

As illustrated in FIG. 3 the geometrical tortuosity bending function, ϑ,from Survey Station 1 to Survey Station 2 is measured two ways, whichcomprise the numerator ∥TVD_(k), NS_(k), EW_(k)∥₂ and the denominatorMD_(k). The denominator is the actual geometry as measured along thewellbore between Survey Station 1 and Survey Station 2, for exampleusing data acquired from a BHA, while the numerator is the idealizedmeasurement based on the square root of the sum of the squares of thevertical distance (TVD_(k)), the North to South distance (NS_(k)) andthe East to West distance (EW_(k)), also taking into consideration theazimuth Az₁ and inclination I₁ of the drill string at Survey Station 1and the azimuth Az₂ and inclination I₂ of the drill string at SurveyStation 2.

To further analyze a bending function in a wellbore, clamping efficiencyparameters may be described in physics-based formulation where forcesacting on the drill pipe 400 are viewed as illustrated in FIG. 4 at thebend in the trajectory designated as (θ, Ø) inclination and azimuth,respectively. The force along the trajectory of the drill string isF_(t), for the tensional or transverse forces on the drill string in thedirection of the wellbore trajectory, while the force normal to thewellbore trajectory at that point is F_(n). The force in the otherdirections from the trajectory of the drill string trajectory at thebend is F_(t)+ΔF_(t), which forces are associated directionally as(θ+ΔØ, α+ΔØ) due to the bending. The weight of the drill string isdesignated W. With these parameters the forces may be combined todescribe the clamping efficiency, analogous to a form of resistance bythe wellbore to the drilling operations due to the drill string'sinteraction with the wellbore geometry:

$\tau^{2} = {\frac{F_{n}^{2}}{F_{t}^{2}} = {{\left( {\Delta\;\varnothing\;\sin\;\theta} \right)^{2} + \left( {{\Delta\;\theta} + {\frac{W}{F_{t}}\sin\;\theta}} \right)^{2}} \approx {\left( {\Delta\;\varnothing\;\sin\;\theta} \right)^{2} + {\Delta\;{\theta^{2}.}}}}}$

FIG. 5 illustrates a process for determining real-time drillingdysfunctions by measuring power-loss of signal propagation associatedwith a drill string. A (first) well is drilled with an instrumenteddrill string wherein the drill string includes a mid-string drilling subunit (for example a torque and tension sub) to acquire, store and sendtime series data by telemetry to the surface 501. A first time series isacquired from a sensor associated with a mid-string drilling sub unit ina wellbore wherein the sensor is below the surface of the earth 503. Asecond time series is acquired from a sensor associated with a drillstring, the drill string in a wellbore, wherein the sensor associatedwith the drill string is on or near the surface of the earth, forexample associated with an acoustic receiver attached to the Kelly orother rig component for acquiring the signal. A geometry of the wellboreis determined, 505, from data acquired from a bottom hole assembly thatis telemetered to the surface. Model parameters that describe thewellbore signal propagation power losses due to geometrical effects aredetermined using the first time series, the second time series and thegeometry of the wellbore to derive model parameters alpha and beta thatcharacterize a power loss of signal propagation for signal travellingthrough the drill string based on attenuation caused by the geometry ofthe wellbore 509 among other dynamic effects. The differentialpower-loss between various sensors at various locations may aidcharacterization. Analysis of the differential power-loss effects ofvarious time-series comparison allows for detection and then mitigationof drilling dysfunctions. A second well may be drilled wherein the drillstring does not include mid string drilling sub units that acquire andsend time series data into the drill string 511. The dynamic state of asecond well drill string in a second wellbore may be determined from athird time series data acquired from a sensor associated with a drillstring in a wellbore, wherein the sensor is on or near the surface ofthe earth (i.e., associated with an acoustic sensor on the Kelly), andthe third time series data are combined with BHA telemetered data andthe model parameters determined from the first well 513. Drillingdysfunctions in drilling the second well may be detected and mitigatedusing the third time series 515, the model parameters derived from thefirst wellbore and the geometry of the second wellbore.

FIG. 6 illustrates a system including a mid-string drilling sub sensor(110) associated with a drill string in a wellbore in a first well foracquiring a first time series 601. A sensor associated with the firstwell drill string for acquiring a second time series wherein the sensoris on a drilling rig or near the surface of the earth 603. A bottom holeassembly 112, 114, 116 associated with the drill string in a well bore102 provides data to determine a geometry 605 of the first wellbore 102.A first computer program module determines model parameters, using thefirst time series, the second time series and the wellbore geometry, toderive model parameters alpha and beta that characterize a power lossfor signal propagation signal travelling through the drill string, 607.Optionally, the system may further comprise a second well drill stringin a well bore 104 wherein the drill string does not include mid stringdrilling sub units that acquire and send time series data into the drillstring, 609. Optionally, the system may also further comprise a secondwell drill string associated sensor 103 wherein the sensor is on or nearthe surface of the earth (for example an acoustic sensor associated withthe Kelly) to provide data for determining the dynamic state of thesecond well drill string in the wellbore from a third time seriesacquired from the sensor combined with the determined model parametersfrom the first well, 611. The system may further comprise a secondcomputer program module determining drilling dysfunctions in drillingthe second well, dysfunctions determined using the determined modelparameters from the first well, the third time series and geometry ofthe second wellbore as derived from the BHA data associated with thesecond drill string, 613. The system may further comprise a thirdcomputer third computer program module for mitigating the drillingdysfunctions in drilling the second well 615.

FIG. 7 illustrates the use of a drilling apparatus for drilling multiplewells 701 comprising a drill rig 101 with a first drill string in a wellbore 102 for drilling a first well with a mid-string sub sensor 110associated with the drilling string for acquiring a first time series703. A second sensor 103 associated with the drill string in a well bore102 wherein the second sensor is on or near the drill rig 101 at thesurface of the earth, the second sensor for acquiring a second timeseries 705. A bottom hole assembly 112, 114, 116 is associated with thedrill string to provide data to determine a geometry of a wellboreassociated with drill string in a well bore 102. The apparatus comprisesa first computer program module for determining model parameters (alpha,beta and optional gamma), using the first time series, the second timeseries and the geometry of the wellbore to derive model parameters alphaand beta that characterize a power loss of signal propagation for signaltravelling through the drill string in the wellbore 709. A second wellmay be drilled wherein the drill string in a wellbore 104 does notinclude a mid-string drilling sub unit 711. A bottom hole assembly 112,114, 116 may be associated with the second drill string in a well bore104 to provide data to determine a geometry of a second wellbore 713 andto provide time series data for comparison with a drill stringassociated sensor on the surface 103, providing a third time series 715in order to derive signal power loss along the drill string in thewellbore and to determine drilling dysfunctions as the well is beingdrilled. After deriving the parameters alpha and beta, these parametersmay be used in the drilling of a second well wherein the geometry dataof the second well, the third time series data (such as from sensor 103)combined with BHA provided time series data to derive power lossinformation related to the second wellbore may be inverted to detect andthen mitigate drilling dysfunctions in drilling operations. In addition,a second computer program module may determine parameter gamma that withalpha and beta may be used to characterize a power loss of signalpropagation for signal travelling in either the first or the seconddrill string. Using combinations of these parameters, a dysfunctiondetection computer program module may determine a dynamic state of thesecond drill string in a wellbore. When a drilling dysfunction isdetected, measures may be taken to mitigate the dysfunction.

Embodiments disclosed herein further include synchronizing times amongmany different sensors and data types that may be ingested by ananalytics engine, for example a processing graph 200 as in FIG. 2. Thedrilling industry has a need to optimize downhole data acquisitionoperations that properly synchronize or correct timing differencesbetween various time series measurements. Considerable efforts in manualoperations are used in the field to synchronize or adjust timedifferences between surface and downhole sensors. However, these manualtime-adjustment operations are not just slow, they are known to open uppotential human errors during the field data acquisition phase.

For example, each sensor may have its own internal clock. In an idealworld, the field operation is able to synchronize the clocks of allsurface and downhole sensors simultaneously to ensure that each clockstarts at the same time and/or all time differences are known. However,in practice, the synchronization is not done during field operations. Asurface sensor often does not synchronize or cannot be synchronized withdownhole sensors, or the clocks of downhole sensors start at differenttime. This non-synchronization of the clocks creates time misalignmentbetween surface and downhole measurements. This timing error may rangefrom minutes to hours.

Another major source of timing error relates to clock drift of eachsensor where a sensor associated clock or timer does not run at the samespeed compared to another clock. That is, after some time the clock“drifts apart” in time from the other clock. The timing of varioussensors may drift relative to other timing devices for any number ofreasons, including physical composition, temperature, pressure, powervariations and timer quality. Timing drift may vary at different ratesarbitrarily. The timing error of the clock drift may range from secondsto minutes.

To correct the timing error due to the non-synchronization of theclocks, the drilling industry often employs a manual method to correlatedownhole data to surface data, assuming surface data to be a referencesignal because surface data are always available, usually convenient touse and synchronize with a main clock, therefore it is often mostconvenient to use a surface associated clock as a reference signal.However, the manual method is labor-intensive, error prone, and lessaccurate depending on a person's judgment and preferences. Since theclock drift is difficult to determine manually, the drilling industryfrequently ignores or approximates this correction.

To avoid the manual corrections of timing errors, embodiments disclosedherein provide automatic methods of one or several steps to correct timemisalignments among surface and downhole data. After the corrections,all the measurements are represented correctly relative to a referenceclock, and therefore all measurements are substantially synchronized intime. Substantially synchronized in time will be understood to meanwithin one or two standard deviations of the measurement error. Thisfacilitates easy and accurate comparisons among all sensors and datasets. The application of time adjustments consists of three keycorrections: 1) correcting for the non-synchronization of the clocksbased on cross-correlation method, 2) correcting for clock drift basedon a dynamic cross-correlation method or a dynamic time warping method,and 3) travel-time path correction between surface and downhole sensorsbased on a “linear moveout correction.” The benefits of this multistepapplication give accurate corrections of timing errors and drasticallyspeed up the processing time, which avoids labor-intensive anderror-prone methods currently employed in the drilling industry. Afterthe corrections, all the measurements are represented correctly relativeto a reference clock.

The following outlines the framework for automatic corrections of timingerrors needed to compensate downhole data. There are numerous time-shiftmethods that can be applied to compute the time corrections. Forexample, it may be preferable to initially use a time-shift method basedon cross-correlation. In signal processing, cross-correlation is ameasure of similarity of two waveforms as a function of a time-lag thatgives a measure of time adjustment that may be applied to one of them.For discrete real time series of f(t) and g(t), the cross-correlation isdefined as (Oppenheim and Schafer, 1989; Telford, et al. 1976):C(_(τ))=Σ_(n=0) ^(N) f(nΔt)*g(nΔt+_(τ)), where C(_(τ)) denotes thecross-correlation function, τ is the displacement of g(t) relative tof(t), termed as the time lag, Δt is the time sample rate, and n is atime sample index.

In some embodiments, the data segment utilizes a time interval to drillat least 2 stands of drill pipe. Each drill pipe is approximately 90feet. It typically takes 3 to 5 hours to complete drilling 2 stands ofdrill pipe. Where there is a new addition of a drill pipe, the values ofthe time series normally reduce to zero, creating a step function. Thecross-correlation of the time series that include those step functionsgives an accurate and robust estimation of the time correction.

For example, f(t) may correspond to surface data and g(t) representsdownhole data. A time shift is found by the maximum of thecross-correlating function of C (_(τ)). The time shift is applied to alldata to correct for non-synchronization of all clocks with the referenceclock (typically a surface clock). As an example, the data length (NΔt)taken into the cross-correlation process may be about 3 to 5 hours at atime, but of course varies by the situation. This process is repeateduntil the end of the data set.

FIG. 8 illustrates an example of time-series data before and after timecorrection of the data, with a surface clock as the reference. Timeseries 801 is transducer data representing Surface measured Revolutionsper Minute (RPM) associated with a surface reference clock. Time series803 is transducer data obtained from a sensor in the wellbore,associated with the drill string, also measuring RPM. An addition of adrill pipe occurs around 75 minutes showing an illustrated example of astep function. After applying cross-correlation as described, a timeshift is obtained to be applied to adjust the time of the wellboresensor RPM data to the surface time series associated reference time.Time series 811 is the same transducer RPM time series data 801associated with a surface reference clock and time series 813 is thewellbore sensor RPM data after the time adjustment determined fromcross-correlation has been applied.

Another method that may additionally be used to correct clock drift usesa dynamic cross-correlation method that is similar to thecross-correlation method. The key difference is the use of a smalleroverlapped-time window to compute a time shift. For example, a typicalwindow size for dynamic cross-correlation is 30 minutes with a 50percent overlapped window; however, the overlap will be dependent on thesituation and the amount of clock drift.

Another method to correct clock drift uses a dynamic time warping method(Hale, 2013) that computes a sample-by-sample time shift. This methodcan give excellent matches between surface and downhole measurements.FIG. 9 illustrates an example before and after clock-drift correction ofdownhole data to a surface reference clock by the dynamic time warpingmethod. Time series 901 is transducer data representing a Surfacemeasured RPM associated with a surface reference clock. Time series 903is transducer data obtained from a sensor in the wellbore, associatedwith the drill string, also measuring RPM. After applying the dynamictime warping as described, a time shift series of adjustments isobtained to be applied to adjust the time of the wellbore sensor RPMdata. Time series 911 is the same transducer RPM time series data 901associated with a surface reference clock and time series 913 is thewellbore sensor RPM data after the time adjustment determined fromcross-correlation has been applied.

Another time adjustment may be added because downhole-sensor locationsvary in depth. For sensors associated with a drilling string, the linearmoveout correction accounts for travel time in which the signal travelsfrom one sensor location in depth to the next sensor and/or to thesurface. The correction ΔT is computed as: ΔT=Z/V, where Z is thedistance from the downhole sensor location to surface, and V is avelocity of signal propagation, which may be the velocity of the steelpipe, the drill string or the velocity of a signal through a conductorof wired pipe. The ΔT correction is dynamic and changes as the depth ofthe sensor increases.

FIG. 10 illustrates an example before and after linear moveoutcorrection of data acquired from downhole transducers, in this caseaccelerometers. Time series 1001, 1003 and 1005 are downholeacceleration time series data acquired from sensors in the wellbore, forexample in or on the drill string. After application of the linearmoveout time adjustment correction described, time series data 1011,1013 and 1015 are illustrated such that the data are substantiallycloser to synchronous in time relative to, for example, a surfaceassociated reference time. Other time adjustments may be added afterthis linear moveout correction, such as the cross-correlation or timewarp methods.

FIG. 11 illustrates a method according to embodiments of the presentdisclosure for automatically adjusting time series data relative to areference time. A first time series is acquired from a downhole sensor1101. A reference time series is acquired, which may be acquired using asurface transducer related time series with a known relationship to areference time 1103. A linear moveout time series is determined toadjust the first time series due to the downhole sensors being variablein depth. The linear moveout time offset adjustment is equal to thedepth of the downhole sensor divided by signal propagation velocity1105. Then the linear moveout offset correction may be applied to thefirst time series 1107. The first time series and the reference timeseries may be cross-correlated to determine a cross-correlation timeoffset correction to apply to the first time series 1109, and thecross-correlation time offset correction is applied 1111 to obtain across-correlation corrected time series.

FIG. 12 illustrates a method according to alternative embodiments of thepresent disclosure for automatically adjusting time series data relativeto a reference time. A first time series is acquired from a downholesensor 1201. A reference time series is acquired, which may be acquiredusing a surface transducer related time series with a known relationshipto a reference time 1203. A linear moveout time series offset adjustmentis determined to adjust the first time series due to the downholesensors being variable in depth. The linear moveout time offsetadjustment is equal to the depth of the downhole sensor divided bysignal propagation velocity or drill string 1205. The linear moveouttime offset adjustment is applied to the first time series to obtain amoveout corrected time series 1207. The first time series and thereference time series are cross-correlated to determine across-correlation time correction to apply to the first time series1209. The cross-correlation time correction is applied to the first timeseries 1211, to obtain a cross-correlation corrected time series. Tocorrect for clock-sensor drift, a dynamic cross-correlation may beapplied to the first time series with the reference time series toobtain dynamic cross-correlation time offset adjustments to apply to thefirst time series 1213. Alternatively, the dynamic time warping processmay be used to determine adjustments to the data for clock drift. Thedynamic cross-correlation time offset adjustments are applied to thecross-correlation corrected time series to obtain dynamically adjustedtime series 1215. In the case dynamic time warp adjustments weredetermined, they can be applied to the first time series.

FIG. 13 illustrates a method according to further embodiments of thepresent disclosure for automatically adjusting time series data relativeto a reference time. A first time series is acquired from a sensor in awellbore 1301. A reference time series is acquired, which may beacquired using a surface transducer related time series with a knownrelationship to a reference time 1303. A linear moveout time seriesoffset adjustment is determined to adjust the first time series due tothe downhole sensors being variable in depth. The linear moveout timeoffset adjustment is equal to the depth of the downhole sensor dividedby signal propagation velocity or drill string 1305. The linear moveouttime offset adjustment is applied to the first time series to obtain amoveout corrected time series 1307. A dynamic time warping may beapplied to the first time series with respect to the reference timeseries to determine a series of dynamic time warp offset adjustments toapply to the first time series 1309. The series of dynamic time warpoffset adjustments are then applied to the first time series to obtain adynamically adjusted time series 1311.

FIG. 14 illustrates a schematic diagram of an embodiment of a system1400 that may correspond to or may be part of a computer and/or anyother computing device, such as a workstation, server, mainframe, supercomputer, processing graph and/or database. The system 1400 includes aprocessor 1402, which may be also be referenced as a central processorunit (CPU). The processor 1402 may communicate and/or provideinstructions to other components within the system 1400, such as theinput interface 1404, output interface 1406, and/or memory 1408. In oneembodiment, the processor 1402 may include one or more multi-coreprocessors and/or memory (e.g., cache memory) that function as buffersand/or storage for data. In alternative embodiments, processor 1402 maybe part of one or more other processing components, such as applicationspecific integrated circuits (ASICs), field-programmable gate arrays(FPGAs), and/or digital signal processors (DSPs). Although FIG. 14illustrates that processor 1402 may be a single processor, it will beunderstood that processor 802 is not so limited and instead mayrepresent a plurality of processors including massively parallelimplementations and processing graphs comprising mathematical operatorsconnected by data streams distributed across multiple platforms,including cloud-based resources. The processor 1402 may be configured toimplement any of the methods described herein.

FIG. 14 illustrates that memory 1408 may be operatively coupled toprocessor 1402. Memory 1408 may be a non-transitory medium configured tostore various types of data. For example, memory 1408 may include one ormore memory devices that comprise secondary storage, read-only memory(ROM), and/or random-access memory (RAM). The secondary storage istypically comprised of one or more disk drives, optical drives,solid-state drives (SSDs), and/or tape drives and is used fornon-volatile storage of data. In certain instances, the secondarystorage may be used to store overflow data if the allocated RAM is notlarge enough to hold all working data. The secondary storage may also beused to store programs that are loaded into the RAM when such programsare selected for execution. The ROM is used to store instructions andperhaps data that are read during program execution. The ROM is anon-volatile memory device that typically has a small memory capacityrelative to the larger memory capacity of the secondary storage. The RAMis used to store volatile data and perhaps to store instructions.

As shown in FIG. 14, the memory 1408 may be used to house theinstructions for carrying out various embodiments described herein. Inan embodiment, the memory 1408 may comprise a computer program module1410, which may embody a computer program product, which may be accessedand implemented by processor 1402. Alternatively, application interface1412 may be stored and accessed within memory by processor 1402.Specifically, the program module or application interface may performsignal processing and/or conditioning and applying analytics to the timeseries data as described herein.

Programming and/or loading executable instructions onto memory 1408 andprocessor 1402 in order to transform the system 1400 into a particularmachine or apparatus that operates on time series data is well known inthe art. Implementing instructions, real-time monitoring, and otherfunctions by loading executable software into a computer can beconverted to a hardware implementation by well-known design rules. Forexample, decisions between implementing a concept in software versushardware may depend on a number of design choices that include stabilityof the design and numbers of units to be produced and issues involved intranslating from the software domain to the hardware domain. Often adesign may be developed and tested in a software form and subsequentlytransformed, by well-known design rules, to an equivalent hardwareimplementation in an ASIC or application specific hardware thathardwires the instructions of the software. In the same manner as amachine controlled by a new ASIC is a particular machine or apparatus,likewise a computer that has been programmed and/or loaded withexecutable instructions may be viewed as a particular machine orapparatus.

In addition, FIG. 14 illustrates that the processor 1402 may beoperatively coupled to an input interface 1404 configured to obtain thetime series data and output interface 1406 configured to output and/ordisplay the results or pass the results to other processing. The inputinterface 1404 may be configured to obtain the time series data viasensors, cables, connectors, and/or communication protocols. In oneembodiment, the input interface 1404 may be a network interface thatcomprises a plurality of ports configured to receive and/or transmittime series data via a network. In particular, the network may transmitthe acquired time series data via wired links, wireless link, and/orlogical links. Other examples of the input interface 1404 may beuniversal serial bus (USB) interfaces, CD-ROMs, DVD-ROMs. The outputinterface 1406 may include, but is not limited to one or moreconnections for a graphic display (e.g., monitors) and/or a printingdevice that produces hard-copies of the generated results.

As illustrated in FIG. 15, nonlimiting embodiments according to thepresent disclosure provide a system for determining a rig-state of adrilling rig during a wellbore drilling operation 1501, which comprisesa computer (1400) comprising a memory (1408) and a processor (1402)1503, a plurality of sensors (103, 110) associated with a wellboredrilling operation 102, 104 for acquiring time series data wherein thedata are formatted for sample and bandwidth regularization andtime-corrected to provide substantially time-synchronized data 1505, aprocessing graph of data-stream networked mathematical operators (FIG.2) that applies continuous analytics to the data at least as rapidly asthe data are acquired to determine dynamic conditions of a plurality ofrig conditions associated with the wellbore drilling operation 1507 anddetermining a rig-state from the plurality of rig conditions 1509.

Other aspects of the system may comprise an output data stream of theprocessing graph that detects and quantifies a drilling dysfunction1511. The processing graph further may output data streams of rigcontrol instructions for the purpose of mitigating a drillingdysfunction 1513. The rig control instructions may be altering RPM,altering weight-on-bit, altering pump pressure, or altering top-driverotational parameters 1515. The processing may also output acharacterization of tortuosity of a wellbore 1517. The acquired timeseries data input to the processing graph may be rotary drillingmeasurements, sliding measurements, reaming measurements, back reamingmeasurements, or tripping related measurements 1519. Processing graphoutput may be an energy loss correction of surface-derived measurementsor a weight-on-bit correction 1521.

In other nonlimiting embodiments, some of which are illustrated in FIG.16, a process for determining a rig-state of a drill rig during awellbore drilling operation 1601 comprises acquiring data from aplurality of sensors associated with a wellbore 1603, formatting theacquired data for sample and bandwidth regularization 1605,time-correcting the data to provide substantially isochronously sampleddata from the plurality of sensors 1607, processing the acquired datathrough a processing graph of networked mathematical operators thatapply continuous analytics to the data at least as rapidly as the dataare acquired to determine dynamic conditions of a plurality of rigoperations associated with the wellbore 1609 and determining a rig-statefrom the plurality of rig operations conditions 1611.

In other aspects determining a rig-state further comprises detection andquantification of a drilling dysfunction 1613. The processing graph mayoutput data streams of rig control instructions to alter rig operationsto mitigate a drilling dysfunction 1615. The rig control instructionsmay be altering RPM, altering WOB, altering pump pressure or alteringtop-drive rotational parameters 1617. The output of the processing graphmay be a characterization of tortuosity of a wellbore 1619. The acquireddata input to the processing graph may be rotary drilling measurements,drill-string sliding measurements, reaming measurements, back reamingmeasurements, or tripping related measurements 1621. Other output datastreams from the processing graph may be an energy loss correction ofsurface-derived input data streams or a weight-on-bit correction 1623.

In still further nonlimiting embodiments a drilling rig apparatus formitigating drilling dysfunctions comprises a drill rig associated with aplurality of sensors providing time series data to a surface-basedaggregator wherein the data are formatted for sample and bandwidthregularization and time-corrected to provide substantiallytime-synchronized data, a computer comprising a memory and a processor,a processing graph of data-stream networked mathematical operators thatapplies continuous analytics at least as rapidly as the time-series areacquired to determine dynamic conditions of a plurality of rigconditions associated with wellbore drilling operation and detecting adrilling dysfunction from the plurality of rig conditions.

In other nonlimiting aspect of the apparatus the processing graphoutputs data streams of rig control instructions for the purpose ofmitigating the detected drilling dysfunction. The rig controlinstructions may be for altering RPM, altering weight on bit, alteringpump pressure or altering top-drive rotational parameters. An outputdata stream of the processing graph may quantify a drilling dysfunction.

In yet more nonlimiting embodiments a computer program product isembodied in non-transitory computer readable media, the computer programproduct adapted to execute a process to mitigate a drilling dysfunctionduring a wellbore drilling operation, which comprises acquiring datafrom a plurality of sensors associated with a wellbore drillingoperation, formatting the acquired data for sample and bandwidthregularization, time-correcting the data to provide substantiallysynchronously sampled data from the plurality of sensors, processing theacquired data through a processing graph of networked mathematicaloperators that apply continuous analytics to the data at least asrapidly as the data are acquired to determine dynamic conditions of aplurality of rig operations associated with the wellbore, detecting adrilling dysfunction from the plurality of rig operations conditions,and outputting drill rig control instructions to mitigate the detecteddrilling dysfunction.

In closing, it should be noted that the discussion of any reference isnot an admission that it is prior art to the present invention,especially any reference that may have a publication date after thepriority date of this application. At the same time, each and everyclaim below is hereby incorporated into this detailed description orspecification as additional embodiments of the present invention.

Although the systems and processes described herein have been describedin detail, it should be understood that various changes, substitutions,and alterations can be made without departing from the spirit and scopeof the invention as defined by the following claims. Those skilled inthe art may be able to study the preferred embodiments and identifyother ways to practice the invention that are not exactly as describedherein. It is the intent of the inventors that variations andequivalents of the invention are within the scope of the claims whilethe description, abstract and drawings are not to be used to limit thescope of the invention. The invention is specifically intended to be asbroad as the claims below and their equivalents.

The invention claimed is:
 1. A system for rig-state determination, thesystem comprising: a plurality of surface sensors on a surface of theearth and associated with a wellbore drilling operation, the pluralityof surface sensors acquiring time series data, the time series databeing formatted for sample and bandwidth regularization andtime-corrected to provide time-synchronized data; a processing graph ofdata-stream networked mathematical operators that applies continuousanalytics to the time-synchronized data at least as rapidly as the timeseries data is acquired by the plurality of surface sensors to determinedownhole dynamic conditions of a plurality of rig conditions associatedwith the wellbore drilling operation, a drilling dysfunction detectablefrom the downhole dynamic conditions of the plurality of rig conditions;and a drilling rig having a rig-state determined from the plurality ofrig conditions.
 2. The system of claim 1, wherein the processing graphoutputs data streams of rig control instructions and mitigate thedrilling dysfunction.
 3. The system of claim 2, wherein the rig controlinstructions are at least one selected from the group consisting of i)altering RPM, ii) altering weight-on-bit, iii) altering pump pressure,and iv) altering top-drive rotational parameters.
 4. The system of claim1, wherein the processing graph outputs a data stream that characterizestortuosity of a wellbore.
 5. The system of claim 1, wherein the timeseries data includes at least one selected from the group consisting ofi) rotary drilling measurements, ii) sliding measurements, iii) reamingmeasurements, iv) back reaming measurements, and v) tripping relatedmeasurements.
 6. The system of claim 1, wherein the processing graphoutputs data streams that are at least one selected from the groupconsisting of: i) an energy loss correction of surface-derived inputdata streams, and ii) a weight-on-bit correction.
 7. A process fordetermining a rig-state of a drill rig during a wellbore drillingoperation, the process comprising: acquiring surface data from aplurality of surface sensors on a surface of the earth and associatedwith a wellbore; formatting the surface data for sample and bandwidthregularization; time-correcting the surface data to provideisochronously sampled data from the plurality of surface sensors;processing the isochronously sampled data from the plurality of surfacesensors through a processing graph of networked mathematical operatorsthat apply continuous analytics to the isochronously sampled data inreal time to determine downhole dynamic conditions of a plurality of rigoperations conditions associated with the wellbore; and determining therig-state from the plurality of rig operations conditions, whereindetermining the rig-state comprises detection and quantification of adrilling dysfunction.
 8. The process of claim 7, wherein the processinggraph outputs data streams of rig control instructions to alter a rigoperation and mitigate the drilling dysfunction.
 9. The process of claim8, wherein the rig control instructions are at least one of the groupconsisting of i) altering RPM, ii) altering weight-on-bit, iii) alteringpump pressure, and iv) altering top-drive rotational parameters.
 10. Theprocess of claim 7, wherein the processing graph outputs a data streamthat characterizes tortuosity of the wellbore.
 11. The process of claim7, wherein the isochronously sampled data includes at least one selectedfrom the group consisting of i) rotary drilling measurements, ii)drill-string sliding measurements, iii) reaming measurements, iv) backreaming measurements, and v) tripping related measurements.
 12. Theprocess of claim 7, wherein the processing graph outputs data streamsthat are at least one selected from the group consisting of: i) anenergy loss correction of surface-derived input data streams, and ii) aweight-on-bit correction.
 13. A drilling rig apparatus for drillingdysfunctions mitigation, the apparatus comprising: a drill rigassociated with a plurality of surface sensors on a surface of the earthand providing time series data to a surface-based aggregator, the timeseries data being formatted for sample and bandwidth regularization andtime-corrected to provide time-synchronized data; and a processing graphof data-stream networked mathematical operators that applies continuousanalytics to the time-synchronized data at least as rapidly as the timeseries data is acquired by the plurality of surface sensors to determinedownhole dynamic conditions of a plurality of rig conditions associatedwith a wellbore drilling operation, a drilling dysfunction determinedfrom the plurality of rig conditions.
 14. The apparatus of claim 13,wherein the processing graph outputs data streams of rig controlinstructions and mitigate the drilling dysfunction.
 15. The apparatus ofclaim 14, wherein the rig control instructions are at least one of thegroup consisting of i) altering RPM, ii) altering weight on bit, iii)altering pump pressure, and iv) altering top-drive rotationalparameters.
 16. The apparatus of claim 13, wherein an output data streamof the processing graph comprises quantification of the drillingdysfunction.
 17. A computer program product embodied in non-transitorycomputer readable media, the computer program product adapted to executea process for drilling dysfunction mitigation during a wellbore drillingoperation, the process comprising: acquiring surface data from aplurality of surface sensors on a surface of the earth and associatedwith the wellbore drilling operation; formatting the surface data forsample and bandwidth regularization; time-correcting the surface data toprovide synchronously sampled data from the plurality of surfacesensors; processing the synchronously sampled data from the plurality ofsurface sensors through a processing graph of networked mathematicaloperators that apply continuous analytics to the synchronously sampleddata in real time to determine downhole dynamic conditions of aplurality of rig operations associated with the wellbore drillingoperation; detecting a drilling dysfunction from the plurality of rigoperations conditions; and outputting drill rig control instructions tomitigate the drilling dysfunction.